摘要
针对基于视觉的无人机姿态测量中的地平线检测问题,提出一种结合图像处理和扩展Kalman滤波的地平线检测算法.图像处理部分先从图像边缘中提取直线来获取候选地平线,再根据图像暗原色先验来选取地平线.扩展Kalman滤波部分将地平线观测模型与机身旋转模型结合起来,获得观测值和残差协方差的先验信息,用于判别检测到的地平线是否正确,并在检测到错误地平线时进行修正.实验结果表明,该算法能够有效地检测地平线,对复杂场景具有鲁棒性.
To detect the horizon in the pose measurement for a visionbased unmanned aerial vehicle （UAV）, we pro pose a horizonextraction algorithm which combines the image processing and extended Kalman filter （EKF）. The image processing part extracts straight lines from the image edges as candidates of horizon; and then, it uses the dark channel prior of the image to decide the horizon. The EKF part of the algorithm combines a horizon line observation model with a fuselage rotation model to obtain the predicted observation and innovation covariance, which are employed for judging whether a correct horizon is detected. Correction will be carried out when wrong horizon is detected. The experimental results show that the proposal algorithm can effectively detect the horizon, and is robust to complex scenes.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2012年第2期225-228,共4页
Control Theory & Applications
基金
国家"973"计划资助项目(2009CB320603)
关键词
地平线检测:边缘检测
暗原色先验
无人机
姿态估计
horizon detection
edge extraction
dark channel prior
unmanned aerial vehicle
attitude estimation
